Plug-in Hybrid Electric Vehicle Fuel Use Reporting Methods and Results
Bibliographic record
Abstract
The Plug-in Hybrid Electric Vehicle (PHEV) Fuel Use Reporting Methods and Results report provides real world test results from PHEV operations and testing in 20 United States and Canada. Examples are given that demonstrate the significant variations operational parameters can have on PHEV petroleum use. In addition to other influences, PHEV mpg results are significantly impacted by driver aggressiveness, cold temperatures, and whether or not the vehicle operator has charged the PHEV battery pack. The U.S. Department of Energy’s (DOE’s) Advanced Vehicle Testing Activity (AVTA) has been testing plug-in hybrid electric vehicles (PHEVs) for several years. The AVTA http://avt.inl.gov/), which is part of DOE’s Vehicle Technology Program, also tests other advanced technology vehicles, with 12 million miles of total test vehicle and data collection experience. The Idaho National Laboratory is responsible for conducting the light-duty vehicle testing of PHEVs. Electric Transportation Engineering Corporation also supports the AVTA by conducting PHEV and other types of testing. To date, 12 different PHEV models have been tested, with more than 600,000 miles of PHEV operations data collected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".